While imaging may accurately identify anatomic narrowing in an artery, it does not assess important underlying functional data, such as the fluid dynamics of blood and the dynamic interaction between the blood flow and artery/plaque wall because it is limited by relatively low resolution, lengthy data acquisition, and high expense. Computational fluid dynamics has become a unique and powerful research tool that is capable of noninvasively accessing in vivo blood flows and quantifying hemodynamics and fluid-structure interaction details in arteries. Recently, patient-specific computational hemodynamics, including reading 3-D image data from CT/MRI scanning, segmenting anatomical structures, generating meshes, and modeling blood flows, has emerged.
The highly nonlinear and multidisciplinary nature of the complex fluid-structure interaction problem poses formidable challenges in existing macroscopic computational methodologies of patient-specific computational hemodynamics.
First, most existing patient-specific computational hemodynamics research employs macroscopic Navier-Stokes solvers, either an in-house program or commercial computational fluid dynamics software, to solve the fluid dynamics. The interfacial dynamic behavior between blood flow and artery/plaque can only be modeled in very complicated ways. Meanwhile, macroscopic Navier-Stokes solvers are often computationally expensive because of the lack of suitability for efficient parallel acceleration.
Second, almost all the current patient-specific computational hemodynamics research relies on external image processing software to extract anatomical structures from CT/MRI imaging data. The computation efficiency is limited as most available software is not parallelized.
Third, data reconstruction and mesh generation are needed to fill the gap between image processing software and a computational fluid dynamics solver, when separately employed, which inevitably introduces extra time, conversion errors, and inaccuracy.